Gene expression profiles to predict recurrence in stage II and III colon cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Morten Thorsteinsson
PROVIDER: E-GEOD-31595 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Purpose: The goal of this experiment was to identify differentially expressed miRNAs between colon cancer patients with and without metachronous metastases. Background: Colon cancer prognosis and treatment are currently based on a classification system still showing large heterogeneity in clinical outcome, especially in TNM-stages II-III. Prognostic biomarkers for metastasis risk are warranted, as development of distant recurrent disease mainly accounts for the high lethality rates of colon cancer. MicroRNAs have been proposed as potential biomarkers for cancer. Furthermore, a verified standard for normalization of the amount of input material in PCR-based relative quantification of miRNA expression is lacking. Methods: A selection of frozen tumor specimens from two independent patient cohorts with TNM-stage II-III microsatellite stable primary adenocarcinomas were used for laser capture microdissection. Next-generation sequencing was performed on small RNAs isolated from colorectal tumors from the Dutch cohort (N=50). Differential expression analysis, comparing in metastasized- and non-metastasized tumors, identified prognostic microRNAs. Validation was performed on colon tumors from the German cohort (N=43) using qPCR. Results: MiR-25-3p and miR-339-5p were identified and validated as independent prognostic markers and used to construct a multivariate nomogram for metastasis risk prediction. The nomogram showed good probability prediction in validation. Additionally, we recommend combination of miR-16-5p and miR-26a-5p as standard for normalization in qPCR of colon cancer tissue-derived microRNA expression. Conclusions: In this international study, we identified and validated an miRNA classifier in primary cancers, and propose a nomogram capable of predicting metastasis risk in microsatellite stable TNM-stage II-III colon cancer.
2014-11-08 | GSE63119 | GEO
Project description:Colorectal cancer is one of the most common cancers in the world. Histological staging is efficient but combination with molecular markers may improve tumors classification. Gene expression profiles have been defined as prognosis predictors among stage II and III tumors but their implementation in medical practice remains controversial. Stage-II tumors have been recognized as a heterogeneous group and high-risk morphologic features have been retained as justifying adjuvant chemotherapy. We propose here the investigation of clinical features and expression profiles from stage II and stage III colon carcinomas without DNA mismatch repair defect. A series of 130 colon cancer samples was retained. Expression profiles were established on oligonucleotide microarrays and processed in the R/Bioconductor environment. Hierarchical then supervised analyses were successively performed applying the data-sampling approach. A molecular signature of seven genes was found to cluster stage III tumors with an adjusted p-values lower than 10^-10. A subgroup of stage-II tumors aggregated this cluster in both series. No correlation was found between with the disease severity but the function of the discriminating genes suggests that tumors have been classified according to their putative response to adjuvant targeted or classic therapies. Further pharmacogenetic studies might document this observation. Expression profile of stage-II colon carcinomas distinguishes two patterns of tumors based on a 7-gene signature. One pattern is very similar to that of stage-III tumors and the corresponding tumors aggregate into a single cluster. Genes function suggests possible tumor determinism in drug response more than in prognosis evolution.
2012-05-10 | E-GEOD-37892 | biostudies-arrayexpress
Project description:Colorectal cancer is one of the most common cancers in the world. Histological staging is efficient but combination with molecular markers may improve tumors classification. Gene expression profiles have been defined as prognosis predictors among stage II and III tumors but their implementation in medical practice remains controversial. Stage-II tumors have been recognized as a heterogeneous group and high-risk morphologic features have been retained as justifying adjuvant chemotherapy. We propose here the investigation of clinical features and expression profiles from stage II and stage III colon carcinomas without DNA mismatch repair defect. A series of 130 colon cancer samples was retained. Expression profiles were established on oligonucleotide microarrays and processed in the R/Bioconductor environment. Hierarchical then supervised analyses were successively performed applying the data-sampling approach. A molecular signature of seven genes was found to cluster stage III tumors with an adjusted p-values lower than 10^-10. A subgroup of stage-II tumors aggregated this cluster in both series. No correlation was found between with the disease severity but the function of the discriminating genes suggests that tumors have been classified according to their putative response to adjuvant targeted or classic therapies. Further pharmacogenetic studies might document this observation.
2012-05-11 | GSE37892 | GEO
Project description:Purpose: A 128-gene signature has been proposed to predict poor outcomes in patients with stage II and III colorectal cancer. In the present study we aimed to validate this previously published 128-gene signature on external and independent data from patients with stage II and III colon cancer.
2011-09-01 | GSE31595 | GEO
Project description:In Western countries, colorectal cancer (CRC) is the third most common cancer in both men and women and the second leading cause of cancer-related deaths (approximately 500,000 deaths annually). For CRC, the tumor stage is the main prognostic factor for survival or relapse after surgery. Current staging is based on the AJCC classification which takes into account tumor size/depth, lymph node involvement and distant metastases. Surgical intervention can cure a significant portion of the patients, especially those that are presented in stages I, II or III (around 75% of patients). Patients in stage II undergo surgery only, whereas stage III patients receive adjuvant chemotherapy. However, a major fraction of these patients either have no need for adjuvant treatment (40% stage III) or would benefit when they were given adjuvant therapy in stage II. Therefore, in the current practice, the majority of patients receive not the optimal treatment. An accurate and reliable method that identifies patients with low and high risk for recurrence could improve the selection for adjuvant therapies in these groups, reducing over- or under-treatment. Molecular profiling of mRNA expression by microarray is an approach to identify relevant genes and gene signatures and has been used already for different types of cancer, like breast and HCC. Most studies, however, resulted in their own classification with a specific separation into groups. Most of these microarray studies show remarkably little overlap and it is difficult to find a clear correlation between the molecular classes and prognosis. The results of the studies seem to be center-dependent because of the different microarray techniques used, the small heterogeneous cohorts that are studied and the different clinical parameters used for the evaluation. Recently, we used a mechanism-driven approach to find a correlation between gene expression in HCC and prognosis in which we tried to overcome most shortcomings (van Malenstein et al. CCR 2010). In this study, we want to apply the approach which we used for HCC now in CRC with the aim to improve the prognosis prediction for patients in AJCC stage II and III. We determined the gene expression by microarray in the colon cancer cell line Caco2 and identified the genes that are differentially expressed under hypoxia (72 hours, 2% O2). Using a bioinformatic approach, we used 5 sets of expression data with corresponding clinical information (training: 3 sets, 234 patients and validation: 2 sets, 322 patients). This resulted in a unique 21 gene set with good performance in retrospective analysis. A second improvement we wanted to achieve is the development of a method that can be applied on FFPE material, which would expand the use of the technique to samples outside the university or research setting. We used the nCounter technique that uses hybridization of labeled probes and quantification without an amplification step. We tested our 21 genes on a cohort of 384 well-characterized patients that were collected between 2004 and 2006 at the University Hospitals of Leuven.
2014-05-22 | GSE31079 | GEO
Project description:In Western countries, colorectal cancer (CRC) is the third most common cancer in both men and women and the second leading cause of cancer-related deaths (approximately 500,000 deaths annually). For CRC, the tumor stage is the main prognostic factor for survival or relapse after surgery. Current staging is based on the AJCC classification which takes into account tumor size/depth, lymph node involvement and distant metastases. Surgical intervention can cure a significant portion of the patients, especially those that are presented in stages I, II or III (around 75% of patients). Patients in stage II undergo surgery only, whereas stage III patients receive adjuvant chemotherapy. However, a major fraction of these patients either have no need for adjuvant treatment (40% stage III) or would benefit when they were given adjuvant therapy in stage II. Therefore, in the current practice, the majority of patients receive not the optimal treatment. An accurate and reliable method that identifies patients with low and high risk for recurrence could improve the selection for adjuvant therapies in these groups, reducing over- or under-treatment. Molecular profiling of mRNA expression by microarray is an approach to identify relevant genes and gene signatures and has been used already for different types of cancer, like breast and HCC. Most studies, however, resulted in their own classification with a specific separation into groups. Most of these microarray studies show remarkably little overlap and it is difficult to find a clear correlation between the molecular classes and prognosis. The results of the studies seem to be center-dependent because of the different microarray techniques used, the small heterogeneous cohorts that are studied and the different clinical parameters used for the evaluation. Recently, we used a mechanism-driven approach to find a correlation between gene expression in HCC and prognosis in which we tried to overcome most shortcomings (van Malenstein et al. CCR 2010). In this study, we want to apply the approach which we used for HCC now in CRC with the aim to improve the prognosis prediction for patients in AJCC stage II and III. We determined the gene expression by microarray in the colon cancer cell line Caco2 and identified the genes that are differentially expressed under hypoxia (72 hours, 2% O2). Using a bioinformatic approach, we used 5 sets of expression data with corresponding clinical information (training: 3 sets, 234 patients and validation: 2 sets, 322 patients). This resulted in a unique 21 gene set with good performance in retrospective analysis. A second improvement we wanted to achieve is the development of a method that can be applied on FFPE material, which would expand the use of the technique to samples outside the university or research setting. We used the nCounter technique that uses hybridization of labeled probes and quantification without an amplification step. We tested our 21 genes on a cohort of 384 well-characterized patients that were collected between 2004 and 2006 at the University Hospitals of Leuven. We compared cells under hypoxia 2% O2 versus 20% O2 conditions. This was done for two cell lines (Caco2 human colorectal adenocarcinoma cell line and HPAC pancreas adenocarcinoma cell line). Each sample has a biological replicate. Samples are hybridized in dye-swap, resulting in 4 hybridizations per cell line.
2014-05-22 | E-GEOD-31079 | biostudies-arrayexpress
Project description:The purpose of this study was to establish a new prognostic model for stage II/III colon cancer. Using public DNA microarray data of colon cancer patients, we created an integrated prognostic model for classifying the patients into high- and low-risk groups based on the expression levels of 55 genes and the KRAS mutation status. For validation, we examined specimens from patients with stage II/III colon cancer who had undergone radical resection at our department, and successfully confirmed prognostic value of our model. We believe that our prognostic model may be clinically helpful to select patients for adjuvant chemotherapy.
2018-03-30 | GSE92921 | GEO
Project description:Combing WHO classification and molecular characteristics to analysis gliomas more and more important. But Grade III glioma samples have a big difference within the group had not been reported as far as we know. In this study, we proposed a model to evaluate diagnosis and prognosis of Grade III glioma patients, and two independent datasets indicated that the model has a good predictive ability. Therefore, this strategy provides a new method to assist diagnosis and treatment of Grade III glioma patients.
2020-07-15 | GSE109857 | GEO
Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration. Of 198 samples, 129 represented the discovery phase and 69 represented the validation phase.
2009-07-02 | E-GEOD-12032 | biostudies-arrayexpress
Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration.
2009-07-02 | GSE12032 | GEO